Data Engineer

• Design, implement, test, and maintain production‑grade backend services and data pipelines in cloud environment
• Write clean, maintainable, and efficient code for large‑scale content ingestion, processing, and delivery
• Debug and resolve complex issues including data correctness, performance bottlenecks, and operational failures
• Participate in code reviews and contribute to shared libraries, reusable components, and internal tooling
• Build and operate pipelines processing high‑volume scholarly content, including XML and other semi‑structured formats
• Implement metadata extraction, transformation, validation, deduplication, and enrichment logic
• Handle imperfect inputs such as non‑standard schemas, missing fields, inconsistent source data, with encoding issues
• Implement ETL/ELT processes for structured and unstructured data
• Integrate internal systems with third-party platforms via APIs and middleware
• Implement data quality checks, automated testing, and observability to ensure data quality, consistency, and performance
• Optimize data pipelines and system performance across systems
• Implement automated workflows for content processing, validation, and policy enforcement
• Reduce manual operational steps through engineering automation
• Ensure compliance with China regulatory requirements, including Cybersecurity Law and PIPL
• Support audits through technical controls such as logging, lineage tracking, and access trails
• Collaborate with cross-functional teams and stakeholders
• Support system migrations, upgrades, and cloud adoption
• Monitor and troubleshoot data and integration issues
• Maintain documentation for data architecture and workflows
• Investigate incidents, perform root‑cause analysis, and implement permanent fixes

Senior Data Engineer, Content Management Systems

• Data Architect supports Chief Data Office by building a scalable, well structured, and AI ready data foundation.
• Design conceptual, logical, and physical data models for the enterprise data warehouse and data marts.
• Participate in defining data standards, modeling conventions, and reusable data patterns.
• Support dataset design for analytics, BI, and machine‑learning workloads.
• Design frameworks for metadata management, data governance, lineage tracking, and asset cataloging.
• Apply basic data mining techniques (profiling, clustering, outlier detection) to better understand source data and inform data model design.
• Use machine learning fundamentals to identify opportunities for optimization (e.g., workload prediction, query pattern analysis, intelligent partitioning).
• Work with data engineers, analysts, and business teams to deliver data requirements.
• Document technical designs, data dictionaries, and operational procedures.

Data Architect – Chief Data Office, IT

• Design, build, and maintain scalable, fault-tolerant data pipelines (batch and/or streaming) for core business and product data.
• Ingest data from diverse sources including APIs, databases, event streams, and third-party services, ensuring high data quality and reliability.
• Design and manage data models and storage layers (data warehouses, data lakes) that support analytics and downstream use cases.
• Partner with analytics, product, and engineering teams to deliver clean, well-documented datasets that enable self-service analytics and experimentation.
• Implement monitoring, logging, and alerting to ensure pipeline reliability, performance, and cost efficiency.
• Enforce data governance best practices, including access control, privacy, documentation, and data lineage.

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Senior Data Engineer, Content Management Systems

Data Architect – Chief Data Office, IT

Data Engineer